The trend today in communications is to digital. The reasons for this trend are several. Digital encoding is more tolerant of the effects of channel noise, digital signals are easily regenerated, digital system errors are simply predicted and controlled, digital systems are highly reliable, and digital systems in general tend to be more economical than analog systems.
Despite the many important advantages associated with digital processing, there is one serious drawback; namely, data bandwidth expansion associated with conversion from continuous analog data to sampled digital data. Data bandwidth expansion means that a larger bandwidth channel must be provided to accommodate the digital data, which significantly increases the channel cost. There has been much interest in recent years in developing methods to reduce the data bandwidth and thus eliminate the expense of large bandwidth communications channels.
Of the methods which have been investigated, those based on linear transforms, such as Fourier, Walsh-Hadamard, Slant, Cosine, and Haar appear to be the most efficient in terms of offering the most compression with the least signal degradation. Transform techniques have only recently been applied to the problem of compressing the bandwidths of picture data. Hardware has been built in Japan, England, and the United States to compress in real time video picture data. However, at this writing, these equipments have been expensive to build and are relatively large in size.
The present invention is a method and apparatus for implementing a real time video bandwidth compression system based on the rationalized Haar transform. The present invention overcomes the cost and size disadvantages of existing systems. The Haar transform was selected over competing transforms (Fourier, Walsh-Hadamard, Slant, etc.) for the following reasons:
1. Only addition and subtraction operations are required (no multiplication operations).
2. The transformation is sparse and has a fast computational algorithm requiring only 2 (N-1) operation for computing an N point transform.
3. The inverse transform has the same form as the direct transform.
4. The transform puts both the low frequency background information and the high frequency edge information in a picture in a form which allows for efficient compression encoding (filtering).
5. The transform can be efficiently mechanized (low cost, small size) in digital form (LSI, hybrid, etc).